app.py

from PIL import Image
import numpy as np
import streamlit as st

from PIL import Image
import numpy as np

# Define function to process uploaded image
def process_image(image):
    # Convert image to numpy array
    img_array = np.array(image)

    # Dummy sidewalk segmentation (replace with your actual segmentation algorithm)
    patch_size = 128
    step = 128

    all_img_patches = []

    for i in range(0, img_array.shape[0] - patch_size + 1, step):
        for j in range(0, img_array.shape[1] - patch_size + 1, step):
            single_patch_img = img_array[i:i + patch_size, j:j + patch_size]
            all_img_patches.append(single_patch_img)

    images = np.array(all_img_patches)

    # Perform your actual image processing here
    # Replace the code below with your segmentation algorithm or any other processing you need
    # This is just a dummy example to show how to process the image
    processed_images = []
    for img in images:
        processed_image = np.mean(img, axis=-1)  # Example: Convert to grayscale
        processed_images.append(processed_image)

    processed_images = np.array(processed_images)

    return processed_images

# Define Shiny app
def main():
    st.title("Sidewalk Segmentation App")
    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

    if uploaded_image is not None:
        # Display uploaded image
        image = Image.open(uploaded_image)
        st.image(image, caption="Uploaded Image", use_column_width=True)

        # Process uploaded image
        with st.spinner("Processing..."):
            segmentation_result = process_image(image)

        # Display segmentation result
        st.image(segmentation_result, caption="Sidewalk Segmentation Result", use_column_width=True)

if __name__ == "__main__":
    main()